Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

Authors: Cheng, Chawathe, LaMacra, Krumm 2005Slides Adapted from Cheng, MobiSys 2005

Review by: Jonathan Odom

Location, Location, Location

• Require accurate location for many applications but GPS only works well outdoors and drains battery

• Wi-Fi APs are commonly found in populated areas and hardware is low cost/low power compared to GPS

• Use Wi-Fi APs as location beacons• Requires map of APs

– Indoor version RADAR has high overhead– Only need accuracy on the order of 10 m

Manhattan (Wigle.net)

War-driving

• Used to create training data• Drive a laptop with Wi-Fi card and GPS

through the streets of city and collect information

• Data – “radio map”– AP unique ID– GPS location of received signal– Signal strength– Response Rate

Experimental Data Sets

Downtown(Seattle)

Urban Residential(Ravenna)

Suburban(Kirkland)

Algorithm - Centroid

• 1st of 3/4 algorithms used• Use arithmetic mean of positions of all AP’s• Not actually use centroid

AP

AP

APEstimate

Algorithm – Fingerprinting SS

• Use 4 closest APs in the Euclidean distance defined by signal strength (k-nearest neighbor)

• Assuming   is the signal strength from the   th AP from the map and   is from the received data

  • Weighting showed only marginal improvement• Allow +/- 2 APs for robustness over time• Based on Bahl 00

Algorithm –Fingerprinting Rank

• All hardware will not give same signal strength• Instead rank signal strength and use correlation with

3 points from radio map

• Where   and   denotes the mean• Based on Krumm 03

  =(-20, -90, -40) ->   =(1,3,2)

Algorithm - Particle Filter

• Particle filters, or a Sequential Monte Carlo method, is a recursive Bayesian estimator

• Empirical data model, using training data– Signal strength as function distance to AP– Response rate as function of distance to AP

• Random walk assumed for motion• Often used for noisy non-linear or non-

Gaussian models

Full Results

• Rank algorithm does not work with sparse APs

0

10

20

30

40

50

60

70

Downtown UrbanResidential

Suburban

Me

dia

n E

rro

r (m

ete

rs) Centroid (Basic)

Fingerprint (Radar)

Fingerprint (Rank)

Particle Filter

More APs Lowers Error

• Rank requires more than 1

AP Reduction

• Localization works well even with 60% APs lost

0

20

40

60

80

100

0% 20% 40% 60% 80% 100%

Med

ian

erro

r (m

eter

s)

AP Turnovers

centroid

particle filter

radar

rank

Adding Noise to GPS Data

• Centroid and particle filter work with noise

Reducing Map Density

• Works well up to 25 mph, 1 scan/sec